Kernel - Photo Repair Crack

def kernel_photo_repair(image, crack_mask): # Define kernel functions def gaussian_kernel(x, y, sigma=1.0): return np.exp(-np.linalg.norm(x - y) ** 2 / (2 * sigma ** 2))

The KPR feature aims to detect and repair cracks in images using advanced kernel-based algorithms. This feature can be integrated into image editing software, allowing users to effortlessly remove unwanted cracks from their photos. kernel photo repair crack

Kernel Photo Repair (KPR) - Crack Detection and Repair j+4)] crack_features = np.array([gaussian_kernel(np.array([i

import numpy as np from sklearn.kernel_ridge import KernelRidge from sklearn.metrics import mean_squared_error sigma=1.0) for x

# Detect cracks crack_detection = np.zeros(image.shape[:2]) for i in range(image.shape[0]): for j in range(image.shape[1]): patch = image[max(0, i-3):min(image.shape[0], i+4), max(0, j-3):min(image.shape[1], j+4)] crack_features = np.array([gaussian_kernel(np.array([i, j]), np.array([x, y]), sigma=1.0) for x, y in patch]) crack_detection[i, j] = np.mean(crack_features)